Large scale item representation matching

ABSTRACT

A two-phase process quickly and accurately identifies representations of the same items within a collection of item representations. In the first phase, referred to as a “blocking phase,” frequency information indicating the frequency with which terms appear within the collection of item representations is used to quickly identify “candidate pairs” (i.e., pairs of item representations that have a relatively high probability of matching). The blocking phase results in a reduced subset of the data for further analysis during the second phase. In the second phase, referred to as a “matching phase,” the candidate pairs are analyzed using fuzzy matching functions to accurately identify “matching pairs” (i.e., representations of the same items).

BACKGROUND

Many data driven applications, including web-based applications,typically rely heavily on and use textual data that originates fromdifferent and diverse data sources. This often results in multiple anddifferent representations of the same items (or entities) in the data.For instance, a data set may include a collection of citations thatrepresent academic publications, and there may be multiple citationswithin the collection that represent the same academic publications.However, because these citations may originate from a variety ofdifferent sources, the various citations that represent the sameacademic publications may differ. In particular, the citations mayinclude numerous variations, such as listing all authors or only partialauthors, using abbreviations, including or excluding different elements(e.g., author, title, venue, volume information, page information,publication date, etc.), including misspellings, and reordering elementsto name a few.

Recognizing these different (and possibly erroneous) representations ofthe same items facilitates consolidating and cleaning the data andcreating cohesion in the data. In some cases, only by matchingrepresentations of items in the data may particular applications beapplied. However, it is difficult to obtain high accuracy in matchingbetween different representations of the same item. The difficulty isfurther exacerbated when matching is to be performed over a largecollection of data.

BRIEF SUMMARY

This summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

Embodiments relate to a two-phase process for quickly and accuratelyidentifying representations of the same items within a collection ofitem representations. In the first phase, or “blocking phase,”information indicative of the frequency with which terms appear withinthe collection of item representations is used to quickly identify“candidate pairs” (i.e., pairs of item representations that have arelatively high probability of matching). The blocking phase results ina reduced subset of the data for further analysis during the secondphase. In the second phase, or “matching phase,” the candidate pairs areanalyzed using fuzzy matching functions to accurately identify “matchingpairs” (i.e., representations of the same items).

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is described in detail below with reference to theattached drawing figures, wherein:

FIG. 1 is a block diagram of an exemplary computing environment suitablefor use in implementing the present invention;

FIG. 2 is a block diagram showing an exemplary system for matching itemrepresentations in accordance with an embodiment of the presentinvention;

FIG. 3 is a flow diagram illustrating an example of the blocking andmatching phases of an embodiment of the present invention;

FIG. 4 is a flow diagram showing an exemplary method for generating aninverted index to facilitate blocking and identify candidate pairs inaccordance with an embodiment of the present invention;

FIG. 5 is a flow diagram showing an exemplary method for identifyingcandidate pairs based on frequency information in accordance with anembodiment of the present invention;

FIG. 6 is a flow diagram showing an exemplary method for using aniterative process to identify candidate pairs in accordance with anembodiment of the present invention; and

FIG. 7 is a diagram illustrating an example of a matching phase for acandidate pair using a library of fuzzy matching functions in accordancewith an embodiment of the present invention.

DETAILED DESCRIPTION

The subject matter of the present invention is described withspecificity herein to meet statutory requirements. However, thedescription itself is not intended to limit the scope of this patent.Rather, the inventors have contemplated that the claimed subject mattermight also be embodied in other ways, to include different steps orcombinations of steps similar to the ones described in this document, inconjunction with other present or future technologies. Moreover,although the terms “step” and/or “block” may be used herein to connotedifferent elements of methods employed, the terms should not beinterpreted as implying any particular order among or between varioussteps herein disclosed unless and except when the order of individualsteps is explicitly described.

Embodiments of the present invention facilitate matching itemrepresentations using a two-phase process that includes a “blockingphase” and a “matching phase.” The process provides a fast and accurateapproach to identify representations of the same items or entitieswithin a data set. For instance, the process may be used to identifycitations corresponding to the same publications or to identifyrepresentations of the same events within a collection of eventinformation (e.g., concerts, plays, movies, etc.). While embodiments ofthe invention will be further illustrated herein primarily in thecontext of academic citations, one skilled in the art will recognizethat the process may also be applied to representations of other typesof items.

The first phase, or “blocking phase,” applies a fast but crude matchingalgorithm over the complete data. The blocking phase results in a highlyreduced subset of the data that contains candidate pairs of itemrepresentations with high probability of being actual matches. Theblocking phase determines candidate pairs using frequency informationindicating the frequency of terms appearing in the collection of itemrepresentations. In some embodiments, an inverted index is generatedthat maps terms to item representations in which the terms appear. Theinverted index also includes an inverse document frequency (IDF) scorefor each term indicating the frequency of the term within the collectionof item representations. The inverted index is then employed to identifythe candidate pairs.

Although the blocking phase quickly identifies pairs of itemrepresentations that have a relatively high probability of matching, theresults are not highly accurate. Accordingly, the second phase, or“matching phase,” operates on the set of candidate pairs determinedduring the blocking phase to identify matching pairs with high accuracy.In the matching phase, the candidate pairs are analyzed using fuzzymatching functions to determine if each candidate pair should beconsidered a matching pair, indicating that the pair of itemrepresentations represent the same item. In some embodiments, thematching phase combines a library of reusable fuzzy matching functionsand a decision tree based classifier. In such embodiments, differentfuzzy matching functions may be applied to different segments of theitem representations based on the suitability of the fuzzy matchingfunctions for the various segments. The classifier then combines theresults of the fuzzy matching functions that are applied to thedifferent segments of the candidate pair to determine if the candidatepair is a matching pair.

Matching representations of the same items facilitates removingredundancy and cleaning the data, as well as allowing differentapplications to be applied. For instance, in the case of academiccitations, identifying matching citations (i.e., citations thatrepresent the same publication) enables a variety of applications, suchas, for instance, performing static ranking for academic web search,grouping together different sources of the same article, and introducinga “cited by” feature.

Embodiments of the invention also provide an approach that is highlyscalable as the design of the blocking phase allows blocking to beperformed over subsets of data by multiple machines. Accordingly, todetermine candidate matches (i.e., blocks) for a set of itemrepresentations A from a set of target item representations B, multiplemachines may be used with each machine examining a subset of both set Aand set B. In other words, not only can blocking be performed over asubset of source information but can also be performed on a subset oftarget information. Results from the various machines may then beaggregated together. This property enables massive scaling, parallelexecution, and distribution of both blocking and matching (sincematching is performed over the results of blocking).

Accordingly, in one aspect, an embodiment of the invention is directedto a computerized method for matching item representations within acollection of item representations. The method includes determiningcandidate pairs of item representations based on frequency informationindicative of the frequency at which terms appear in the collection ofitem representations. The method also includes matching itemrepresentations by analyzing the candidate pairs using one or more fuzzymatching functions.

In another embodiment of the invention, an aspect is directed to one ormore computer-readable media embodying computer-useable instructions forperforming a method of matching item representations from a collectionof item representations. The method includes extracting terms from thecollection of item representation and determining frequency informationindicative of the frequency with which the terms appear within thecollection of item representations. The method also includes generatingan inverted index mapping the terms to the item representations in whichthe terms appear, wherein the inverted index further includes thefrequency information for the terms. The method further includesdetermining one or more candidate pairs of item representations usingthe inverted index based on terms shared between item representationsand frequency information associated with the terms. The method stillfurther includes identifying one or more matching pairs of itemrepresentations by analyzing the candidate pairs using the fuzzymatching algorithms.

A further aspect of the invention is directed to a computerized systemincluding one or more computer-readable media embodying softwarecomponents for matching item representations from a collection of itemrepresentations. The software components include a blocking componentthat identifies candidate pairs of item representations based onfrequency information associated with terms shared between the candidatepairs. The software components also include a matching component thatidentifies matching pairs of item representations by analyzing thecandidate pairs using one or more fuzzy matching algorithms.

Having briefly described an overview of the present invention, anexemplary operating environment in which various aspects of the presentinvention may be implemented is described below in order to provide ageneral context for various aspects of the present invention. Referringinitially to FIG. 1 in particular, an exemplary operating environmentfor implementing embodiments of the present invention is shown anddesignated generally as computing device 100. Computing device 100 isbut one example of a suitable computing environment and is not intendedto suggest any limitation as to the scope of use or functionality of theinvention. Neither should the computing device 100 be interpreted ashaving any dependency or requirement relating to any one or combinationof components illustrated.

The invention may be described in the general context of computer codeor machine-useable instructions, including computer-executableinstructions such as program modules, being executed by a computer orother machine, such as a personal data assistant or other handhelddevice. Generally, program modules including routines, programs,objects, components, data structures, etc., refer to code that performparticular tasks or implement particular abstract data types. Theinvention may be practiced in a variety of system configurations,including hand-held devices, consumer electronics, general-purposecomputers, more specialty computing devices, etc. The invention may alsobe practiced in distributed computing environments where tasks areperformed by remote-processing devices that are linked through acommunications network.

With reference to FIG. 1, computing device 100 includes a bus 110 thatdirectly or indirectly couples the following devices: memory 112, one ormore processors 114, one or more presentation components 116,input/output ports 118, input/output components 120, and an illustrativepower supply 122. Bus 110 represents what may be one or more busses(such as an address bus, data bus, or combination thereof). Although thevarious blocks of FIG. 1 are shown with lines for the sake of clarity,in reality, delineating various components is not so clear, andmetaphorically, the lines would more accurately be grey and fuzzy. Forexample, one may consider a presentation component such as a displaydevice to be an I/O component. Also, processors have memory. Werecognize that such is the nature of the art, and reiterate that thediagram of FIG. 1 is merely illustrative of an exemplary computingdevice that can be used in connection with one or more embodiments ofthe present invention. Distinction is not made between such categoriesas “workstation,” “server,” “laptop,” “hand-held device,” etc., as allare contemplated within the scope of FIG. 1 and reference to “computingdevice.”

Computing device 100 typically includes a variety of computer-readablemedia. By way of example, and not limitation, computer-readable mediamay comprise Random Access Memory (RAM); Read Only Memory (ROM);Electronically Erasable Programmable Read Only Memory (EEPROM); flashmemory or other memory technologies; CDROM, digital versatile disks(DVD) or other optical or holographic media; magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other storage medium that can be used to encode and store desiredinformation and be accessed by computing device 100.

Memory 112 includes computer-storage media in the form of volatileand/or nonvolatile memory. The memory may be removable, nonremovable, ora combination thereof. Exemplary hardware devices include solid-statememory, hard drives, optical-disc drives, etc. Computing device 100includes one or more processors that read data from various entitiessuch as memory 112 or I/O components 120. Presentation component(s) 116present data indications to a user or other device. Exemplarypresentation components include a display device, speaker, printingcomponent, vibrating component, etc.

I/O ports 118 allow computing device 100 to be logically coupled toother devices including I/O components 120, some of which may be builtin. Illustrative components include a microphone, joystick, game pad,satellite dish, scanner, printer, wireless device, etc.

Referring now to FIG. 2, a block diagram is provided illustrating anexemplary system 200 for matching representations of items in accordancewith an embodiment of the present invention. It should be understoodthat this and other arrangements described herein are set forth only asexamples. Other arrangements and elements (e.g., machines, interfaces,functions, orders, and groupings of functions, etc.) can be used inaddition to or instead of those shown, and some elements may be omittedaltogether. Further, many of the elements described herein arefunctional entities that may be implemented as discrete or distributedcomponents or in conjunction with other components, and in any suitablecombination and location. Various functions described herein as beingperformed by one or more entities may be carried out by hardware,firmware, and/or software. For instance, various functions may becarried out by a processor executing instructions stored in memory.

The system 200 facilitates matching representations of the same itemswithin a collection of item representations 202. The collection of itemrepresentations 202 may generally include representations of items fromdata that originates from different and diverse data sources. As aresult, the collection 202 may include representations of the same itemsthat differ in content and form. Accordingly, the system 200 facilitatesidentifying representations of the same items.

The collection of item representations 202 may be maintained by one ormore computing devices that are accessible by an extraction component204. The extraction component 204 scans the item representations withinthe collection 202 and extracts data regarding terms appearing in theitem representations and frequency information indicative of thefrequency with which the terms appear within the collection 202. Invarious embodiments of the invention, terms extracted from the itemrepresentations may include individual words and/or phrases.

As will be described in further detail below, the frequency informationmay be used by the blocking component 206 as a measure of a term'simportance for matching. In particular, terms that are common and appearfrequently within the collection of item representations 202 are notlikely to provide a good indication that item representations sharingthose common terms are matching. Conversely, terms that are rare andappear less frequently within the collection of item representations 202are likely to provide a good indication that item representationssharing those rare terms are matching. In some embodiments, thefrequency information comprises an IDF score calculated for each termbased on the frequency with which each term appears in the collection202. In further embodiments, an inverted index is generated that mapsterms to the item representations containing the terms and includes theIDF score for each term.

The data extracted by the extraction component 204 is used by theblocking component 206 to identify candidate pairs 208. As such, theblocking component 206 quickly reduces the large collection of items 202into a subset of candidate pairs 208 that have a relatively higherprobability of being a matching pair (i.e., a pair of representations ofthe same item). To identify candidate pairs, the blocking component 206examines terms shared between pairs of item representations taking intoaccount frequency information associated with each of the terms. If apair of item representations share terms having a sufficient level ofimportance (based on frequency information), the pair is considered acandidate pair.

The matching component 210 analyzes each of the candidate pairsidentified by the blocking component to determine if a candidate pair isa matching pair with high accuracy. The matching component 210 appliesfuzzy matching functions to each candidate pair to determine if thecandidate pair represent the same item. In some embodiments, a singlefuzzy matching function may be applied to a candidate pair to determineif the candidate pair is a matching pair. In other embodiments,corresponding segments with each item representation may be identified,and a suitable fuzzy matching function may be applied to each segment. Adecision tree classifier then combines the results of the fuzzy matchingfunctions for each of the different segments to determine if thecandidate pair is a matching pair.

The overall process for identifying matching item representations willnow be further illustrated using a specific example in the context ofacademic citations with reference to FIG. 3. As shown in FIG. 3, anincoming citation 302 is received at the blocking phase 304. In thepresent example, the incoming reference is a citation for an academicpublication: “Pratt J. and Johnson R., Joining Decision Trees,Artificial Intelligence Vol. pp. 344-356.” The blocking phase 304employs frequency information from a collection of citations to identifycitations within the collection that have a probability of matching theincoming citation 302. Accordingly, the outcome of the blocking phase304 is a number of blocked citations 306, which comprise a subset of allcitations within the collection. Each of the blocked citations 306identified during the blocking phase 304 in conjunction with theincoming reference 302 comprise a candidate pair having a probability ofrepresenting the same academic publication. The candidate pairs (i.e.,each block citation 306 and the incoming citation 302) are analyzedduring the matching phase 308 using one or more fuzzy matchingalgorithms to identify citations that match the incoming citation 302.In the present example, the first citation in the blocked citations 306is identified as a matching citation 310.

As indicated previously, in some embodiments of the invention, aninverted index is generated to facilitate the blocking phase. Withreference to FIG. 4, a flow diagram is provided showing a method forgenerating an inverted index to facilitate blocking and identifyingcandidate pairs for matching in accordance with an embodiment of thepresent invention. Initially, as shown at block 402, a collection ofitem representations is preprocessed. Preprocessing may include a numberof different steps to normalize and prepare the data for furtherprocessing within various embodiments of the invention. For instance,preprocessing may include removing delimiters, such as commas andquotes, and lowercasing all characters in the data.

As shown at block 404, the preprocessed item representations are parsedto identify and extract terms from the item representations. In someembodiment, individual words may extracted from the item representationsand identified as terms. In other embodiments, phrasal extraction mayalso be employed to identify extract phrases, such as “tropical storm”or “human embryo.” Each phrase may then be treated as a discrete termand included in the list of terms for the item representations. In someembodiments of the invention, stop-word filtering may be applied toidentify and filter out stop words (i.e., words that are unimportant todetermining matching pairs such as “the” and “a”).

After parsing the item representations to identify terms, an IDF scoreis determined for the extracted terms, as shown at block 406. The IDFscores are used as a measure of the general importance of terms formatching item representations. The IDF score for each term is a functionof the frequency of term in the collection of item representations. Thegreater the frequency of a term in the collection (i.e., a common word),the less likely the term will provide a good indication of matchingbetween item representations. Conversely, the lower the frequency of aterm in the collection (i.e., a rare word), the more likely the termwill provide a good indication of matching between item representations.

An inverted index is generated using the frequency information, as shownat block 408. The inverted index maps the extracted terms to the itemrepresentations containing the terms. Additionally, the inverted indexincludes the IDF score calculated for each of the extracted terms. Asindicated previously, the inverted index may be used in the blockingphase to quickly and efficiently determine candidate pairs for analysisduring the matching phase.

As discussed previously, frequency information is used during theblocking phase to determine the likelihood that pairs of itemrepresentations are matching pairs. In particular, if a pair of itemrepresentations has rare terms (i.e., terms have a low frequency withinthe collection of item representations) in common, there is a greaterlikelihood that the item representations are a matching pair. A varietyof algorithms may be employed to determine candidate pairs during theblocking phase using the frequency information. By way of example onlyand not limitation, FIG. 5 illustrates a flow diagram showing one method500 for determining candidate pairs based on frequency information inaccordance with an embodiment of the present invention. As shown atblock 502, pairs of item representations having one or more terms incommon are identified. An aggregate IDF score is then computed for eachidentified pair by aggregating the IDF score for all terms in common foreach pair, as shown at block 504. In some embodiments, an inverted indexsuch as that generated in accordance with the method 400 of FIG. 4 maybe employed to determine the aggregate IDF score for each pair.

The aggregate IDF score for a pair of item representations is thenemployed as an indicator of the likelihood that the pair qualifies as amatching pair. In particular, the aggregate IDF score for each pair iscompared against a predetermined threshold at block 506 to determine ifthe pair should be considered a candidate pair for analysis during thematching phase. If the aggregate IDF score for a pair is greater thanthe threshold, the pair is identified as a candidate pair, as shown atblock 508. Conversely, if the aggregate IDF score for a pair is lessthan the threshold, the pair is not identified as a candidate pair, asshown at block 510.

In another embodiment of the invention, an algorithm is employed duringthe blocking phase that is designed on an IDF-based inverted index (suchas that generated in accordance with the method 400 of FIG. 4) andintroduces “shortcuts” that accelerate the overall running time of theblocking phase. Instead of aggregating IDF scores for all terms incommon between a pair of item representations as that performed in themethod 500 of FIG. 5, an iterative process is employed in which termsfor a given item representation are prioritized based on importance(i.e., IDF score) and considered in turn based on this priority. Withreference to FIG. 6, a flow diagram is provided showing a method 600 forblocking using an iterative algorithm providing “shortcutting” inaccordance with an embodiment of the invention. The method 600 may beperformed for each item representation in a collection to identifyblocked item representations for each given item representation.Accordingly, as shown at block 602, a target item representation isidentified. The terms for the item representation are sorted in order oftheir respective IDF scores such that the more important terms (i.e.,terms having higher IDF scores) appear first in the sorted order, asshown at block 604. Additionally, an item representation having at leastone term in common with the target representation is identified, asshown at block 606.

The process continues at block 608, at which the next term in the sortorder is selected as the current term. If this is the first iteration,the first term, which has the highest importance based on IDF score, isselected as the current term. As shown at block 610, whether the currentterm exists in both of the item representations is determined. If theterm is not common between the two item representations, the processreturns to block 608, at which the next term in the sort order isselected. Alternatively, if the current term exists in both itemrepresentations, the term's IDF score is added to an aggregate IDF scorefor the pair, as shown at block 612. Again, if this is the firstiteration, the aggregate IDF score will be the IDF score for the firstterm shared by the item representations. The aggregate IDF score is thencompared against a threshold at block 614 to determine whether the pairshould be considered a candidate pair for further matching analysis. Ifthe aggregate score is above the threshold, the pair is identified as acandidate pair, as shown at block 616. Conversely, if the aggregatescore is below the threshold, a determination is made whether thecurrent term is the last term from the target item representation, asshown at block 618. If the current term is the last term, the pair isnot identified as a candidate pair at block 620.

Alternatively, if the current term is not the last term, a prediction ismade regarding whether a threshold will ever be reached for the currentpair of item representations given the shared terms already consideredand the remaining terms from the target representation. Thisconsideration allows shortcutting if it is predicted the threshold willnot be reached for the pair. To perform this predication, the remainingterms from the target representation are assumed to be shared betweenthe item representations, as shown at block 622. Additionally, a maximumpossible aggregate score is computed by adding the remaining terms' IDFscores to the current aggregate IDF score, as shown at block 624. Thismaximum possible aggregate IDF score is then compared against thethreshold, as shown at block 626. If the maximum possible aggregate IDFscore is less than the threshold, the pair is not identified as acandidate pair, as shown at block 620. Alternatively, if the maximumpossible aggregate score is greater then the threshold, the processiterates to the next term in the sort order at block 608 and the processis repeated using the next term as the current term.

As discussed previously, after candidate pairs have been identifiedduring the blocking phase, the candidate pairs are analyzed using fuzzymatching functions to accurately identify those candidate pairs thatrepresent matching pairs. Any of a variety of fuzzy matching functionsmay be employed with the scope of embodiments of the present invention.By way of example only and not limitation, the fuzzy matching functionsmay include: string edit distances (e.g., Levenshtein, Needleman-Wunsh,Smith-Waterman distance), Jaccard distance, TF-IDF cosine similarity,Soft TF-IDF, SoundEX distance. These functions may be applied based oncharacters, tokens, character n-grams, or token n-grams.

In some embodiments, a single fuzzy matching function may be applied tothe item representations as a whole or to a portion of the itemrepresentations to determine if the item representations are matching.In further embodiments, however, different segments of the itemrepresentations may be identified and fuzzy matching algorithms suitablefor matching the different segments may be applied. For instance, acitation for a publication may include segments such as author, title,and venue. Each of these segments have different characteristics. Forexample, some segments may be more likely to include abbreviations,changes in word order, or other variations. Accordingly, fuzzy matchingfunctions may be selected for each of the segments based on each fuzzymatching functions suitability for handling such characteristics andvariations. If different fuzzy matching functions are applied to varioussegments of item representations, a decision tree classifier may combinethe results of the various fuzzy matching functions to determine if acandidate pair is a matching pair.

By way of example, FIG. 7 illustrates a matching phase using a libraryof fuzzy matching functions 704 applied to different segments of acandidate pair 702. In the example of FIG. 7, the candidate pair 702being analyzed is a pair of citations that include the segments: title,author, and venue. Accordingly, fuzzy matching functions from thelibrary 704 are applied to each of the segments based on the suitabilityof each fuzzy matching function for the different segments.Additionally, a fuzzy matching function is applied to the itemrepresentations as a whole. The results of the fuzzy matching functionsare combined using a decision tree classifier 706 to determine whetherthe candidate pair is a matching pair.

As can be understood, embodiments of the present invention provide atwo-phase process for quickly and accurately identifying representationsof the same items within a collection of item representations. Thepresent invention has been described in relation to particularembodiments, which are intended in all respects to be illustrativerather than restrictive. Alternative embodiments will become apparent tothose of ordinary skill in the art to which the present inventionpertains without departing from its scope.

From the foregoing, it will be seen that this invention is one welladapted to attain all the ends and objects set forth above, togetherwith other advantages which are obvious and inherent to the system andmethod. It will be understood that certain features and subcombinationsare of utility and may be employed without reference to other featuresand subcombinations. This is contemplated by and is within the scope ofthe claims.

1. A computerized method for matching item representations within acollection of item representations, the method comprising: determiningcandidate pairs of item representations based on frequency informationindicative of the frequency at which terms appear in the collection ofitem representations, wherein the frequency information comprises an IDFscore determined for each term based on the respective term's frequencyof use within the collection of item representations, and whereindetermining candidate pairs comprises determining an aggregate IDF scorefor pairs of item representations by adding the IDF scores for termsshared by each pair of item representations and comparing the aggregateIDF score for each pair of item representations against a threshold todetermine if each pair of item representations qualifies as a candidatepair; and matching item representations by analyzing the candidate pairsusing one or more fuzzy matching functions.
 2. The computerized methodof claim 1, wherein the item representations comprise citations ofpublications.
 3. The computerized method of claim 1, wherein the termsappearing in the collection of item representations comprises at leastone of individual words and phrases.
 4. The computerized method of claim1, wherein determining candidate pairs comprises sorting terms from atarget item representation based on IDF score; and iterating through thesorted terms in a manner such that at each iteration: (1) an aggregateIDF score is determined based on whether a current term and previouslyiterated terms are shared between the target item representation and asecond item representation and (2) the aggregate IDF score is comparedagainst a threshold to determine if the target item representation andsecond item representation comprise a candidate pair.
 5. Thecomputerized method of claim 4, wherein determining candidate pairsfurther comprises, at each iteration, predicting whether it is possiblefor the target item representation and second item representation to beidentified as a candidate pair by assuming remaining terms from thetarget item representation are shared by the second item representation.6. The computerized method of claim 1, wherein matching itemrepresentations comprises identifying different segments within the itemrepresentations and applying fuzzy matching functions suitable for eachof the different segments.
 7. The computerized method of claim 6,wherein matching item representations further comprises employing adecision tree classifier to combine results from the fuzzy matchingalgorithms.
 8. One or more computer-readable storage media embodyingcomputer-useable instructions for performing a method of matching itemrepresentations from a collection of item representations, the methodcomprising: extracting terms from the collection of item representation;determining frequency information indicative of the frequency with whichthe terms appear within the collection of item representations, whereinthe frequency information comprises an IDF score calculated for eachterm; generating an inverted index mapping the terms to the itemrepresentations in which the terms appear, wherein the inverted indexfurther includes the frequency information for the terms; determiningone or more candidate pairs of item representations using the invertedindex based on terms shared between item representations and frequencyinformation associated with the terms by determining aggregate IDFscores for pairs of item representations based on terms shared betweeneach pair of item representations and comparing the aggregate IDF scoresagainst a threshold; and identifying one or more matching pairs of itemrepresentations by analyzing the candidate pairs using one or more fuzzymatching algorithms.
 9. The one or more computer-readable storage mediaof claim 8, wherein the item representations comprise citations ofpublications.
 10. The one or more computer-readable storage media ofclaim 8, wherein the terms comprises at least one of individual wordsand phrases.
 11. The one or more computer-readable storage media ofclaim 8, wherein determining one or more candidate pairs comprises:sorting terms from a target item representation based on the IDF scorefor each term; and analyzing whether a second item representation andthe target item representation comprise a candidate pair by iteratingthrough the sorted terms and determining at each iteration whether thetarget item representation and second item representation are acandidate pair and whether further iterations are necessary.
 12. The oneor more computer-readable storage media of claim 8, wherein identifyingone or more matching pairs comprises: identifying different segmentswithin the item representations; determining a suitable fuzzy matchingfunction for each of the different segments; applying the fuzzy matchingfunctions; and employing a decision tree classifier to combine theresults of the fuzzy matching functions to identify the one or morematching pairs.
 13. A computerized system including one or morecomputer-readable media embodying software components for matching itemrepresentations from a collection of item representations, the softwarecomponents comprising: a blocking component that identifies candidatepairs of item representations based on frequency information associatedwith terms shared between the candidate pairs, wherein the blockingcomponent identifies candidate pairs of item representations bydetermining aggregate IDF scores based on the frequency informationassociated with terms shared between pairs of item representations andcomparing the aggregated IDF scores against a threshold; and a matchingcomponent that identifies matching pairs of item representations byanalyzing the candidate pairs using one or more fuzzy matchingalgorithms.
 14. The computerized system of claim 13, wherein thematching component identifies matching pairs by identifying differentsegments within the item representations and applying fuzzy matchingfunctions suitable to the different segments and employing a decisiontree classifier to combine the results of the fuzzy matching functionsto identify matching pairs.